The increasing frequency and intensity of natural disasters such as floods, wildfires, and landslides demand rapid monitoring systems capable of providing real-time situational awareness and reliable communication support. Conventional disaster monitoring methods rely heavily on terrestrial infrastructure, which often becomes damaged or unavailable during emergencies. This project proposes an Artificial Intelligence enabled Low Earth Orbit (LEO) satellite prototype designed to autonomously monitor environmental conditions and assist emergency communication during disaster scenarios. The proposed system integrates onboard image acquisition, environmental sensing, and intelligent data processing within a CubeSat-inspired architecture. A camera payload captures surface images which are analyzed using an embedded Artificial Intelligence model trained to identify disaster indicators such as smoke, fire, and flooding. Telemetry sensors continuously monitor satellite health parameters including temperature, orientation, and power status. A solar energy simulation subsystem ensures sustainable operation, while long-range LoRa communication enables reliable data transmission between the satellite and a ground station. Experimental evaluation of the prototype demonstrates effective real-time disaster detection and stable long-distance wireless communication with low power consumption. Upon identifying a disaster event, the system automatically generates alerts and transmits critical information to the ground station dashboard, supporting faster response decisions. The proposed model provides a cost-effective educational nanosatellite platform that combines embedded systems, artificial intelligence, and space communication concepts, offering strong potential for future disaster management applications and scalable multi-satellite deployments.